High-Speed communications systems typically use a wide band channel where the transmission is achieved using Radio Frequency (RF) carriers. RF transmission as it exists today either uses closely spaced narrow band multiple carriers or a small number of carriers containing baseband modulated signals.
An example of a closely spaced narrow band multiple carrier system is the Orthogonal Frequency Division Multiplexing (OFDM) system, which uses a large number of RF carriers with each carrier carrying two base band modulation signals (I/Q). Since OFDM uses orthogonal carriers, the transmission does not suffer any Inter-Frequency-Interference (IFI). Also, the data processing at the receiver uses a simple Fast Fourier Transform (FFT) technique. Due to the orthogonality of the RF carriers, an OFDM transmission system is more robust to Inter-Symbol-Interference (ISI). However, when ISI exists, the system requires the use of a Cyclic Prefix as an overhead and a channel equalizer to handle ISI.
An example of a small number of carriers containing baseband modulated signals is the “Kelquan” system based on the teachings presented in U.S. Pat. Nos. 5,956,372 and 8,233,564 where closely spaced non-orthogonal frequencies are used to create baseband modulated signals which are carried in a small number of RF carriers as I/Q channels over a wideband bandwidth. In this system, the data is recovered optimally after the IFI suppression using a Neural Network Matched Filter. This system requires no overhead, but needs a robust equalizer to handle ISI.
In both of the above scenarios, the performance of high-speed digital transmission suffers high degradation due to the effects of channel impairments. Specifically, the channel impairments, which include Inter-Symbol-Interference and the leakage of I/Q modulated signals, which are sent over each of the RF carriers, significantly degrades the Bit Error Rate (BER) performance. The ISI is caused by the change of bandwidth of the frequencies of specific symbols, spilling over to the next set of symbols, or to the previous set of symbols. The leakage of I/Q signals on each other is caused by the imperfect phase alignment between the transmit and receiver carrier phases. In OFDM systems, the leakage of I/Q signals can be more predominant in wireless channels as opposed to wireline channels. In a small number of carrier based systems, both wireline and wireless channels experience leakage of I/Q due to imperfect phase imbalance. As the transmit systems carry large data rates, the sensitivity to these channel impairments become significant.
In accordance with the invention described herein, an Artificial Neural Network (ANN) based demodulator is shown that handles the ISI and I/Q leakage due to phase imbalance as a single apparatus. The novel design of this demodulator simplifies the adaptive demodulator complexity and improves the data recovery process significantly in terms of Bit Error Rates (BER).
While the invention is applicable to broader transmission channels, the preferred embodiment is a system that has a small number of RF carriers for transmission over a wideband channel.
Traditional systems use two different systems to handle these two channel impairments, where each system requires separate training time during initialization. When both impairments are handled separately with each requiring its own training time, the computation time to optimize the design with appropriate correction coefficients increases. Also, there could be bottlenecks in the design process to achieve optimal system performance, when these two impairments are handled sequentially, one after another. The teaching of this invention is directed to an integrated demodulator that avoids this pitfall by simultaneously handling both I/Q imbalance and ISI with a single training sequence. This process develops the necessary coefficients for an ANN demodulator to achieve optimum performance.
In summary, this invention teaches the design of an Artificial Neural Network based Demodulator that achieves the following functions at the receiver:                1. Compensates for the I/Q imbalance due to carrier phase miss-alignment between the transmitter and receiver        2. Equalizes the ISI introduced by the channel        3. Equalizes the ISI introduced by the channel filter        4. Recovers the original data which was used for modulation at the sending side.        
The proposed invention achieves significant advantages over traditional methods of handling transmission impairments, for example,                It reduces the computational complexity of the demodulation process using single operation as opposed to multiple operations.        It leads to more accurate and robust handling of channel impairments at the receiver due to integrated operation instead of sequential operations.        It increases the battery life of mobile apparatus (particularly useful to handheld devices) by extending the mean time before failure.        
Equalization techniques broadly support handling transmission impairments over different channels: wireline communications or wireless communications or highly dispersive channels. The transmission impairments can be different in different channels.
In wireline channels, the channel equalization is designed to handle ISI and reflections. The concept of equalization relates to the loss compensation for the equalizer as a figure of merit, which is used to derive the performance of the data recovery at the receiver. Since the distance between the sending side and the receiving side is fixed, the channel characteristics are known ‘a priori’ and it is possible to a use a Minimum Mean Squared Error (MMSE) equalizer to minimize the effect of ISI. When the channel transfer function is unknown, it is imperative to use an adaptive MMSE equalizer.
There are implementations of equalizers used to handle ISI based on Least Mean Squared Error (LMSE). This equalizer performs well in minimizing the effect of ISI as long as the phase variation on the channel is low. Although a LMSE equalizer works well in a minimum phase channel, its performance is very limited in a channel with spectral nulls. In such cases, the convergence of an LMS equalizer is not guaranteed and ISI effects cannot be minimized.
Another alternative to handle the ISI problem is the use of a Decision Feedback Equalizer (DFE). While the DFE outperforms the LMS, it is more complex than the LMS equalizer. Furthermore the DFE suffers from an error propagation problem and therefore is only used at very high SNR scenario. The MMSE, LMSE and DFE equalizers can only minimize the effect of ISI on the performance, but cannot handle the I/Q phase alignment problem. The present invention of an integrated demodulator which both equalizes ISI and compensates I/Q/imbalance outperforms a LMSE equalizer even for non-minimum phase variation in the channel.
In wireless channels, the channel equalization is more complex when handling ISI due to rapid changes in channel behavior because of mobility and channel fading. The channel can be modeled as a highly dispersive channel and will require a more complex operation to reduce or eliminate the ISI effects. These channels tend to be more time invariant, but are adaptive and therefore, the channel equalizers tend to be adaptive to compensate and adjust for the slow variations of the channel.
Some of the equalization methods used to handle wireless channels include:
a. The method to nullify or mitigate the effect of channel response by employing a training period to initialize the channel equalizer that has a simple adaptive system. Some techniques in this category include also a blind equalization technique without a training period by employing different and possible-to-estimate channel characteristics.
b. For OFDM channels, which use a narrow frequency band, the channel equalization reduces the problem to handle flat fading or a frequency non-selective system.
c. For handling channel fading, techniques such as multiple transmission of the same information over independent channels and waiting for the fading to recede before sending have been exploited. The ultimate measure is the improvement of probability of error in fading channels.
In summary, there are many teachings to design channel equalizers based on neural networks to handle one selective parameter at a time. As such, designing the equalizer to handle multiple effects on the channel is more optimum and robust than handling parameters one at a time which can cause delay in processing to achieve optimization.
The proposed teaching in this invention is to demonstrate designing the inventive demodulator to handle the effects of more than one parameter simultaneously with a single training sequence while achieving optimum performance for data recovery at the receiver.